Summary of Flykd: Graph Knowledge Distillation on the Fly with Curriculum Learning, by Eugene Ku
FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning
by Eugene Ku
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FlyKD method aims to improve the efficiency of student models in Knowledge Distillation by enabling the generation of virtually unlimited pseudo labels. This is achieved through a combination of FlyKD and Curriculum Learning, which alleviates the optimization process over noisy pseudo labels. Compared to vanilla KD and LSPGCN, FlyKD outperforms them empirically. The paper also explores the benefits of Curriculum Learning in improving optimization over noisy pseudo labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FlyKD is a method that makes student models faster and more deployable by transferring knowledge from teacher models. It does this by generating many pseudo labels, which helps the student model learn better. This process is tricky because it uses noisy information, but FlyKD makes it easier with its combination of techniques. The results show that FlyKD works better than other methods. |
Keywords
» Artificial intelligence » Curriculum learning » Knowledge distillation » Optimization » Student model
